Here are the Top 10 Uses for AI in 2018

Introduction

More and more, artificial intelligence (AI) is being employed to help automate all aspects of your digital life. Many of the use cases are obviously AI-driven applications, but there are also other, less obvious solutions. In this article we’ll take a look at the top ten commercial applications areas for AI in 2018.

The Top 10 AI applications of 2018 illustrate just how far #AI has evolved in the last five years. @icecreamlabs

Click to Tweet

1. Voice Search & Virtual Assistants

The past year has seen the launch of multiple consumer interfaces and devices which employ an interactive voice interface. From the Siri interface on Apple iPhones to the Alexa and Google Home devices, the application of voice search has evolved to become a real part of our lives. Unlike text search, which uses keyboard input to interpret search intent, the voice search engines require the interpretation of spoken word to understand what a user is asking. This technology has been evolving for decades, but it’s the maturity of machine learning and the compute power of the cloud which has enabled these solutions to become a reality today. The accuracy of voice search is still evolving and many consumers find more entertainment value than commercial value in their interaction with these devices.

The growth of Siri, Alexa and the other voice interfaces is breaking down the barriers for adoption and acceptance of AI-enabled solutions. This is the reason that we decided to put voice search at the top of our 2018 list.

2. Ridesharing

Ridesharing solutions such as Uber and Lyft are growing in popularity as consumers find them a useful way to get around. The AI-enabled decision engines in these ridesharing tools look at both current demand and historical usage trends to help predict when and where consumers are going to request a ride. The result is that the tools can adjust pricing in what they call “Surge Pricing Models” to help attract drivers during peak periods and to provide a competitive alternative to other transportation choices. The effect is that Uber, for example, has created a transportation marketplace with a bunch of suppliers (e.g. drivers) competing to pickup customers. In the process, Uber is optimizing the fees paid by consumers and the fees paid to the drivers. This would not be possible without a sophisticated AI learning model behind it.

3. Autonomous Navigation

On the topic of transportation, machine learning is also emerging as an enabling solution to the needs of autonomous navigation. Indoor autonomous navigation was solved a decade ago for mobile robots operating in controlled environments (such as warehouses and office buildings) without the need for artificial intelligence. The autonomous navigation of automobiles requires 10 to 100 times more processing power to function. The key problem for autonomous automobiles are both the speed at which the vehicles operate and the uncontrollability of the environment (including factors such as weather, time of day and all of the various road hazards).

Another problem for deploying machine learning systems is the large amount of training data that is required. Tesla solved the training data acquisition problem by deploying their very first models with a complete set of sensors, which captured on-road data as their first customers drove their vehicles manually. This strategy turned every Tesla driver/owner into a data acquisition device for the company. As a result, Tesla received millions of miles of real-life driving data to help train their autonomous driving AI. Other automotive companies are only now beginning to reach the same level of capability as Tesla.

Navigating autonomously is a difficult problem, due in part to the fact that the vehicles are carrying human payloads. However, autonomous trucks are successfully operating on the interstate highways, transporting cargo. Tu Simple is one company who has successfully developed an autonomous rig. All of the autonomous vehicles in operation still require a licensed human driver in attendance.

4. Recommendation Engines

If you listen to music or watch movies, then you are familiar with at least one of the recommendation engines that have emerged in the last couple of years. From Pandora for music to Netflix for movies, the use of machine learning to automate content recommendations is one of the most mature applications for AI. Both Pandora and Netflix now have millions of subscribers who use their service everyday. With that usage data, these services are able to train their machine learning models about all of the content consumed by their users. The result is that the models can predict the types of music or movies that a user might be interested in, based on their consumption patterns and the patterns of other consumers. The magic is the recommendations aren’t programmed, but rather build their intelligence over time directly from the trends that the system sees across all of the user base. Pandora uses 400 musical characteristics of each song to help the AI engine categorize a new song.

5. Home Automation

One of the exciting and emerging application areas for machine learning is home automation. I gave up a long time ago trying to get my kids to turn off lights, fans and other electrical devices in our house. However, with home automation, smart devices can learn usage patterns and then adapt themselves over time. The Nest thermometer is one of the most successful home automation solutions to adopt machine learning to predict and optimize home heating and cooling cycles. The Nest is connected to the Internet and can leverage the processing power of the cloud to compare the heating and cooling cycles of all of the Nest customers. The result is that Nest can implement the optimal heating and cooling cycles which use the least amount of electricity. When combined with weather and location data, Nest can even preemptively heat or cool your house.

6. Face Detection

Face detection is another machine learning application which has matured in the last five years. Face detection is enabling the autonomous tagging of images on-line. The driving force for this application has been social media tools such as Facebook, Instagram, Pinterest and SnapChat. All of these social media tools operate on images shared online by their users. When users “tag” images with the names of their friends, the system learns how to associate user names with their faces. What makes this work is that the users do all of the hard work of training the system, as they manually tag their friends.

7. Navigation

Navigation is another machine learning application which has matured to the point that mobile phone users now consider it to be a utility. Mobile phones are connected to the cloud in real-time, and track the position, direction and speed of travel of the phone. This has enabled solutions such as Waze and Google Maps to use mobile phones as real-time sensors for traffic conditions and to map the paths that users take between destinations. As a result, the path data is used to train machine learning models about the optimal paths at any time of the day, and to alert all users to accidents and heavy traffic in real time.

8. Email

A decade ago, email spam was a real problem for any email user. What started as “blacklisting” of email addresses in each individual in-box, quickly evolved into an escalating war between the spammers and the email recipients. The evolution of AI, however, has changed this war. Email tools employing AI are now able to not only verify the sender’s email, but to also read the content of the email and determine if the content is spam or a legitimate email of interest to the receiver. In addition, on-line email providers like Gmail are able to categorize and sort email into folders based on the content of the email. The result is a much more efficient email experience by users and better control over unsolicited email.

9. Banking and Finance

The world of banking and finance has seen the emergence of AI as a tool to help automate many financial processes. One application which benefits both the bank and the end user is fraud protection. AI tools which monitor credit card usage in real time can now accurately predict when a transaction is fraudulent. These AI-based tools help to minimize or limit any damage or loss, which in term helps to keep rates low. In addition, credit decisions which used to take days or weeks can now be processed in seconds or minutes. This improves the consumer experience and enhances on-line commerce.

10. Grading

Finally, AI solutions are emerging to help our over worked teachers do their jobs more efficiently. AI is being deployed to help teachers evaluate and grade student work. It’s now possible to review student writing with a plagiarism checker, and to cross check online references and quoted material for accuracy. These tools can also easily rank student writing for grammar and verify writing levels.

Summary

As you can see, artificial intelligence is already being deployed to help enhance our lives in many ways. The applications which we just examined have all come to market in the last five years and are quickly maturing as consumers find them useful. As a result, the state of the art for AI and machine learning is quickly evolving and innovation is happening to help make the most difficult applications possible. We’re still far from a general artificial intelligence, but very specific applications of AI are changing our lives every day for the better.

“When you have a product catalog with hundreds of thousands or millions of product records, it can be a daunting task to validate all of the data attributes and related image data,” said IceCream Labs founder and CEO Madhu Konety. “Product data comes in continuously from your suppliers and gets reconciled with existing product records in your product catalog. The one thing that you don’t want to do is to lose a sale because the product record is incomplete or doesn’t reflect the actual product that you’re selling. We’ve solved this problem for some of the largest e-commerce vendors in the market and are now offering a new version of our solution for e-commerce providers of all sizes.”

Introducing CatalogIQ

CatalogIQ is the foundation of the IceCream Labs Cognitive Commerce solution. With CatalogIQ, companies can ensure their product catalog data is complete and that it’s ready to use with customers. CatalogIQ is especially beneficial for product catalogs with a large number of products. Using machine learning models, CatalogIQ can adapt to changes in product data over time as users add and delete products or merge new supplier data. Regardless of what is being sold, CatalogIQ can quickly learn about product attributes and begin to validate the integrity of the meta-data.

“We’ve built machine learning models which can identify incorrect attributes and even ensure that product images accurately match the product being sold,” added Konety. “Our first customer is one of the largest US ecommerce providers, and their catalog is so large that it’s impossible for a human to validate all of the data in a timely fashion. With CatalogIQ, they are now able to stay in front of almost continuous product data changes.”

CatalogIQ enables businesses to understand the data integrity of their product catalog. CatalogIQ scans the data to identify which elements are incomplete, inaccurate or invalid. It can identify suspect duplicate product records. To help improve search results, CatalogIQ assesses the SEO-readiness of the text components of a product record.

The result is a thorough understanding of the quality of data in the product catalog. Catalog Managers can now identify all incorrect data and cleanse the information before publication. CatalogIQ gives Catalog Managers the confidence that product information is accurate.

The payback is two-fold: First, catalog managers can now validate that supplier product data is accurate and quickly isolate records which need to be cleansed. This reduces the product data integration timeline and improves the time to market for adding new products to the catalog. Secondly, catalog managers can be assured that bad data doesn’t end up in front of real customers while they are shopping, which can impact both revenue and brand reputation.

CatalogIQ is available now at www.icecreamlabs.com. Customers who leverage CatalogIQ will be able to benefit from the upcoming automation that Cognitive Commerce will enable across the entire ecommerce data management spectrum.

About IceCream Labs

IceCream Labs is the only AI powered platform which provides on-demand Intelligent Merchandising solutions for e-commerce retailers, brands and marketplace sellers. We help you realize the maximum potential of your product catalog by boosting the quality of your product content to create an immediate impact on revenues and operations.

Several of the world’s largest retailers have benefited from our Intelligent Merchandising platform. Deep learning applications on the platform have delivered results with absolute precision, accelerating revenues 4X for our customers.

Our new catalog data quality platform consumes product data coming in through various sources. Applications on the platform continuously process and profile the content quality of over 100 million products and 50 million images empowered by our big data algorithms. This data is interpreted by our multiple patent pending Deep Learning models to cleanse, enrich and optimize your product content. The output of the models can be integrated seamlessly with your existing solutions to help you reach your business goals.

At IceCream Labs, we believe in the value of technology and its ability to disrupt traditional business models. Our Culture – like technology – is open and without any boundaries. We believe in the power of providing simplicity while managing the complexity behind it, by keeping our focus constantly on Innovation and Execution.